What is the talk about?

• Why do we need data viz?
• A quick review of fundamentals: Variables and appropriate visualization
• Markers of Good* data viz
• Reading Closely
• Common Errors
• Resource Reccomendations

What is not covered?

This is my EVIL Plan:

• Tell them what is good visualization
• Tell them what is bad visualisation
• But do not reveal how to actually make good viz in a consistent and efficient manner. [Cue Diabolical Laughter from Despicable ME]

Can you tell me something?

Following is the number of time I day dream about food, recorded over two weeks:

 [1] 24 25 26 19 24 30 27 29 26 27 28 26 27 20

Here is the number of times I tried to complete this presentation over the same period of time:

 [1] 26 24 29 14 26 28 21 28 28 35 31 19 24 19

It is not easy and intuitive to look at number and say something about it. Even if summary stats are provided. Let us look at this more closely.

You all know of the quartet - Anscombe and friends

   x1 x2 x3 x4    y1   y2    y3    y4
1  10 10 10  8  8.04 9.14  7.46  6.58
2   8  8  8  8  6.95 8.14  6.77  5.76
3  13 13 13  8  7.58 8.74 12.74  7.71
4   9  9  9  8  8.81 8.77  7.11  8.84
5  11 11 11  8  8.33 9.26  7.81  8.47
6  14 14 14  8  9.96 8.10  8.84  7.04
7   6  6  6  8  7.24 6.13  6.08  5.25
8   4  4  4 19  4.26 3.10  5.39 12.50
9  12 12 12  8 10.84 9.13  8.15  5.56
10  7  7  7  8  4.82 7.26  6.42  7.91
11  5  5  5  8  5.68 4.74  5.73  6.89

# A tibble: 4 × 3
  variable  mean    sd
  <chr>    <dbl> <dbl>
1 y1        7.50  2.03
2 y2        7.50  2.03
3 y3        7.5   2.03
4 y4        7.50  2.03

Things are not always as they seem

Clearly, looking at data helps

• Helps the build intuitive understanding of the data
• Identify patterns, sometimes expected, sometimes unexpected
• Convey a lot of information in a concise an accessible and memorable manner
• All the points are true for the people generating as well as consuming a visualization

Variables - What?

A record of any measurement of interest.

• Total fans in every room of APU, Bhopal
• Proportion of left handed people in every class at APU, Bhopal
• Number of students that ate lunch in mess on every wednesday
• Ice Cream flavours sold everyday at a retail shop
• Names of people who think potato belongs in Biryani

Variables - Type

1. Continuous: Amount of protien in every meal (Hello to those gym going fools)
2. Discrete/Count: Number of buffalos owned by each household in Balasore
3. Ordinal: How was the Biryani? - very bad, bad, ok, good, very good
4. Categorical: Types of Biryani

Can you come up with any fun examples?

Categorical and Ordinal Data - Continency and Frequency Tables

Categorical and Ordinal Data - Bar Chart

Categorical and Ordinal Data - Bar Chart (2.1)

Figure 2: Acess to Primary Health Centre in Gujarat - Stacked

Categorical and Ordinal Data - Bar Chart (2.2)

Figure 3: Acess to Primary Health Centre in Gujarat - Grouped

Categorical and Ordinal Data - Bar Chart (2.3)

Figure 4: Acess to Primary Health Centre in Gujarat - Proportion

Framing

1. “Framing can effect how we feel about the numbers shown”
2. The same statistic can be shown in a negative frame or positive frame.
3. “Us reports mortality rates of child heart surgery, while UK provides survival rates.”
4. “Ideally both negative and positive frames should be presented if we want to provide impartial information…”

Framing - Table

Comparing Proportions

from: The Art of Statistics

1. The international Agency for Research on Cancer classified Group1 processed measts as carcinogenics.
2. This lead to poor headlines.
3. “50g of processed meat a day was associated with an increased risk of bowel cancer by 18%”
4. Would you stop eating bacon?

Absolute or Relative

The example mentions 18%. Sure sounds a big enough proportion to worry about getting bowel cancer. BUT, this is not Absolute difference in risk. This is relative risk to people who consume bacon every day.

1. People who do not consume processed eats have 6% chance of getting bowel cancer
2. Similar, or the same people, if ate processed eats every day of their life would have 18% more chance of bowel cancer.
3. This does not mean: 6% + 18 %
4. This means: 6*1.18 ~ 7%

Continuous and categorical Variables

Village population across districts

Continuous Variable

Distance to closest town

Continuous Variables

Comparing population and geographical area of villages

Can we identify an effective vs a bad visualization

Can we identify an effective vs a bad visualization

Tufte on Visualization

“Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data. (Tufte, 1983, p. 51).”

Markers of a good visualization

• Not just about how it looks, though this makes the graph memorable
• Depends who is looking at it
• Why are they looking at it - what is expected out of the chart
• Essentially, you need both, good taste as well as an understanding of how human visual perception works. The latter can be learned with practice and in relatively less time than the first. Good taste needs to be developed and it takes time.

A discussion on this figure

Identifying features of bad visualizations

The following problems are distinct but can appear in various combinations in a given figure.

• Strictly Aesthetic
• Substantive - the data presented is somehow off
• Perceptual

Back to Democracy - what are the good things?

What if I tell you

• These are are not the responses from a longitudinal survey
• Actually, it is the same question asked to people born in different decades, i.e., different age groups.
• Cherry on top it was not a binary question
• Identifying substantive problems require understanding of underlying data of a chart, being observant of any transformations and consequent effects, etc..

A good samaritan

One for the finance bros

What if it was shown this way

How to address such issues - Healy’s Alternative

Garden variety mistakes - Single variable distributions

Garden variety mistakes - Single variable distributions

Garden variety mistakes - two or more variable distributions

Alternatives from Wilke

for multi variable distribution

Alternatives from Wilke

for multi variable distribution

Alternatives from Wilke

for multi variable distribution

Alternatives from Wilke

for multi variable distribution

Garden Variety mistake - Unordered Barcharts

Resources -

• Edward Tufte
• Kieran Healy
• Claus Wilke
• Cedric Scherer
• Tanya Shapiro
• Cara Thompson
• Nicola Rennie